Commit 61a7dc0e authored by wooway777's avatar wooway777
Browse files

issue/666 - Standardized test imports

parent 406c9668
......@@ -5,9 +5,13 @@ sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
import torch
import infinicore
from framework.base import BaseOperatorTest, TensorSpec, TestCase
from framework.runner import GenericTestRunner
from framework.utils import is_broadcast
from framework import (
BaseOperatorTest,
TensorSpec,
TestCase,
GenericTestRunner,
is_broadcast,
)
# Test cases format: (shape, size_or_scale_factor, mode, align_corners_or_None, input_strides_or_None)
......
......@@ -5,9 +5,13 @@ sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
import torch
import infinicore
from framework.base import BaseOperatorTest, TensorSpec, TestCase
from framework.runner import GenericTestRunner
from framework.utils import is_broadcast
from framework import (
BaseOperatorTest,
TensorSpec,
TestCase,
GenericTestRunner,
is_broadcast,
)
# Test cases format: (shape, size_or_scale_factor, align_corners_or_None, input_strides_or_None)
# infinicore.nn.functional.upsample_bilinear is deprecated in favor of interpolate(mode='bilinear')
......
......@@ -5,9 +5,13 @@ sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
import torch
import infinicore
from framework.base import BaseOperatorTest, TensorSpec, TestCase
from framework.runner import GenericTestRunner
from framework.utils import is_broadcast
from framework import (
BaseOperatorTest,
TensorSpec,
TestCase,
GenericTestRunner,
is_broadcast,
)
# Test cases format: (shape, size_or_scale_factor, input_strides_or_None)
# infinicore.nn.functional.upsample_nearest is deprecated in favor of interpolate(mode='nearest')
......
......@@ -5,8 +5,7 @@ sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
import torch
import infinicore
from framework.base import BaseOperatorTest, TensorSpec, TestCase
from framework.runner import GenericTestRunner
from framework import BaseOperatorTest, TensorSpec, TestCase, GenericTestRunner
# Test cases format: (input_shape, input_strides_or_None, N)
_TEST_CASES_DATA = [
......
......@@ -5,9 +5,13 @@ sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
import torch
import infinicore
from framework.base import BaseOperatorTest, TensorSpec, TestCase
from framework.runner import GenericTestRunner
from framework.utils import is_broadcast
from framework import (
BaseOperatorTest,
TensorSpec,
TestCase,
GenericTestRunner,
is_broadcast,
)
# Test cases format: (in_shape, in_strides_or_None, dim_or_None, unbiased_or_None, keepdim_or_None)
# var computes variance along dim(s) or overall
......
......@@ -5,9 +5,13 @@ sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
import torch
import infinicore
from framework.base import BaseOperatorTest, TensorSpec, TestCase
from framework.runner import GenericTestRunner
from framework.utils import is_broadcast
from framework import (
BaseOperatorTest,
TensorSpec,
TestCase,
GenericTestRunner,
is_broadcast,
)
# Test cases format: (in_shape, in_strides_or_None, dim_or_None, unbiased_or_None, keepdim_or_None)
# var_mean returns (var, mean)
......
......@@ -5,8 +5,7 @@ sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
import torch
import infinicore
from framework.base import BaseOperatorTest, TensorSpec, TestCase
from framework.runner import GenericTestRunner
from framework import BaseOperatorTest, TensorSpec, TestCase, GenericTestRunner
# Test cases format: (vec1_shape, vec2_shape, vec1_strides_or_None, vec2_strides_or_None)
# vdot(a, b) — conjugate dot product for 1-D vectors
......
......@@ -5,8 +5,7 @@ sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
import torch
import infinicore
from framework.base import BaseOperatorTest, TensorSpec, TestCase
from framework.runner import GenericTestRunner
from framework import BaseOperatorTest, TensorSpec, TestCase, GenericTestRunner
# Test cases format: (condition_shape, cond_strides_or_None, x_shape_or_None, y_shape_or_None)
# infinicore.where can be used as where(condition, x, y) or where(condition) returning indices.
......
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